Financial portfolio optimization is a challenging problem. First, the problem is multiobjective (i.e.: minimize risk and maximize profit) and the objective functions are often multimodal and non smooth (e.g.: value at risk). Second, managers have often to face real-world constraints, which are typically non-linear. Hence, conventional optimization techniques, such as quadratic programming, cann...

NAG Libraries have many powerful and reliable optimizers which can be used to solve large portfolio optimization and selection problems in the financial industry. Below is an introduction into the notation and techniques used in portfolio optimization. We discuss some sample problems and present help in choosing an appropriate NAG optimizer. Finally, there is a section on handling transaction c...

In this work introduce an adaptive method of portfolio optimization. The basic idea is to describe essential movements of the stock price using a hidden Markov model and to calculate the optimal portfolio using a recursive algorithm. The portfolio optimization is adaptive in the sense that the standard EM–algorithm fits the model to historical data, which improves the portfolio performance.

This paper addresses about an approach that suggests for stock portfolio optimization using the combination of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Capital Asset Pricing Model (CAPM). Stock portfolio optimization aims to determine which of the stocks to be added to a portfolio based on the investor’s needs, changing economic and market conditions. In order to construct an efficient...

We discuss a class of risk-sensitive portfolio optimization problems. We consider the portfolio optimization model investigated by Nagai in 2003. The model by its nature can include fixed income securities as well in the portfolio. Under fairly general conditions, we prove the existence of optimal portfolio in both finite and infinite horizon problems.

In this article, we apply the mean-expected tail loss (ETL) portfolio optimization to the consensus temporary earnings forecasting (CTEF) data from global equities. The time series model with multivariate normal tempered stable (MNTS) innovations is used to generate the out-of-sample scenarios for the portfolio optimization. We find that (1) the CTEF variable continues to be of value in portfol...

While the unconstrained portfolio optimization problem can be solved efficiently by standard algorithms, this is not the case for the portfolio optimization problem with additional real world constraints like cardinality constraints, buy-in thresholds, roundlots etc. In this paper we investigate two extensions to Evolutionary Algorithms (EA) applied to the portfolio optimization problem. First,...

We analyze the implications of short-selling and margin purchase constraints for management compensation and portfolio optimization under moral hazard. First, looking at the manager’s problem, we show that her active portfolio (that is, net of the benchmark) will not be independent of the benchmark design. We solve analytically for the benchmark composition that maximizes e¤ort expenditure. Ana...